Google Cloud コンソールに、データセットへの入力に使用されたソースファイルと、各ファイルのデータ分割方法の一覧が表示されます。
インポートしたデータをソースファイルごとに削除することもできます。
該当する機能はありません。ネイティブ データセットにソースファイルの情報は記録されません。
モデルの評価
新しいテストセットまたは既存のデータセットからの評価の実行をサポートします。
新しいテストセットに対する評価のみを実行できます。
オペレーションのキャンセル
データセットのインポートとモデル作成オペレーションのキャンセルをサポートします。
長時間実行オペレーションをキャンセルすることはできません。
アップグレード後のGoogle Cloud コンソールの動作
1 つ以上のリソースをアップグレードすると、 Google Cloud コンソールは AutoML API ではなく Cloud Translation API を使用するように切り替わります。そのため、 Google Cloud コンソールで新しいデータセットを作成すると、デフォルトでネイティブ データセットが作成されます。この変更はプロジェクト レベルで行われるため、プロジェクトの他のユーザーにもこの変更が表示されます。レガシー データセットを作成するには、レガシー データセットの作成オプションを選択するか、AutoML API を使用する必要があります。
新しいカスタムモデルをトレーニングする場合、 Google Cloud コンソールではデータセットに応じて AutoML API または Cloud Translation API が使用されます。レガシー データセットの場合、コンソールでは AutoML API を使用してレガシーモデルが作成されます。ネイティブ データセットの場合、 Google Cloud コンソールでは Cloud Translation API を使用してネイティブ モデルが作成されます。
Cloud Translation API
Cloud Translation API でネイティブ リソースを管理するには、正しいリソース ID で適切な API を呼び出すようにコードを更新する必要があります。たとえば、AutoML API を呼び出してレガシー リソース ID を参照するコマンドがある場合は、Cloud Translation API を呼び出してネイティブ リソース ID を参照するようにコマンドを更新する必要があります。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[],[],null,["# Upgrade AutoML resources\n========================\n\nIf you have existing resources that were created by using the AutoML API,\nyou can upgrade those resources to manage them through the\nCloud Translation - Advanced API without any service interruptions or additional\ncosts. During the upgrade, Cloud Translation copies your AutoML\n(legacy) resources, such as datasets and models, and creates new\nCloud Translation (native) resources through the Cloud Translation API.\n\nWe recommend that you use Cloud Translation because future enhancements to\ndatasets and customs models will apply only to Cloud Translation. Upgraded\nresources can take advantage of those future enhancements such as additional\nlanguage pair support.\n\nThere's no requirement to upgrade your resources. You can still use the\nAutoML API, which will continue to be available.\n\nUpgrade considerations\n----------------------\n\nAfter upgrading, your native and legacy resources exist together but are managed\nby different APIs. To access and manage the upgraded resources, you must use the\nCloud Translation API, not the AutoML API.\n\nThe native resources are identical to legacy resources except for their resource\nIDs. Cloud Translation doesn't make any changes to legacy resources. You can\ncontinue to work with your legacy resources as before.\n\nYou can choose to upgrade some or all of your resources. When you upgrade a\ndataset, any models that are associated with that dataset are also automatically\nupgraded. Only models without an underlying dataset (like in cases where the\nassociated dataset was deleted) can be manually upgraded on their own.\n\n### Differences between legacy and native resources\n\nThe following table outlines the differences between legacy and native\nresources.\n\n### Google Cloud console behavior post upgrade\n\nIf you upgrade at least one resource, the Google Cloud console switches to\nusing the Cloud Translation API instead of the AutoML API. So, when you create\nnew datasets in the Google Cloud console, you create native datasets by\ndefault. This change happens at the project level, so other users of your\nproject also see this change. To create a legacy dataset, you must select the\ncreate legacy dataset option or use the AutoML API.\n\nWhen training new custom models, the Google Cloud console uses the\nAutoML API or Cloud Translation API, depending on the dataset. For legacy\ndatasets, the console uses the AutoML API to create legacy models. For\nnative datasets, the Google Cloud console uses the Cloud Translation API to\ncreate native models.\n\n### Cloud Translation API\n\nTo manage native resources through the Cloud Translation API, you need to update\nyour code to call the correct APIs with the correct resource IDs. For example,\nif you have commands that call the AutoML API and reference legacy resource\nIDs, you need to update those commands to call the Cloud Translation API and\nreference the native resource IDs.\n\nFor more information about the Cloud Translation API, see the\n[projects.locations.datasets](/translate/docs/reference/rest/v3/projects.locations.datasets) and\n[projects.locations.models](/translate/docs/reference/rest/v3/projects.locations.models) resources.\n\nUpgrade resources\n-----------------\n\nUse the Google Cloud console to upgrade existing AutoML resources to\nCloud Translation resources.\n\n1. Go to the Cloud Translation console.\n\n [Go to the\n Translation page](https://console.cloud.google.com/translation)\n2. Click **Datasets** to view your existing datasets.\n\n3. Click **Upgrade** to open the **Upgrade dataset** pane, which lists the\n datasets that you can upgrade.\n\n When you upgrade a dataset, any model that's associated with that dataset\n is also automatically upgraded.\n4. Select the datasets to upgrade, and then click **Start upgrading**.\n\n On the **Datasets** page, the Google Cloud console lists your upgraded and\n legacy datasets in separate tables.\n5. To manually upgrade models, in the navigation pane, click **Models** to view\n your existing models.\n\n You can manually upgrade only models without an underlying dataset (like if\n the model's associated dataset was deleted).\n6. Click **Upgrade** to open the **Upgrade model** pane.\n\n7. Select the models to upgrade, and the click **Start upgrading**.\n\n On the **Models** page, the Google Cloud console lists your upgraded and\n legacy models in separate tables.\n\nAfter you upgrade your resources, consider making the following changes:\n\n- Update existing code to use the Cloud Translation API and newly created resources. For more information, see [Create and manage datasets](/translate/docs/advanced/automl-datasets) and [Create and manage models](/translate/docs/advanced/automl-models).\n- For translation predictions, use the Cloud Translation API instead of the AutoML API. For more information, see [translating text with a custom\n model](/translate/docs/advanced/translating-text-v3).\n\nDelete legacy resources\n-----------------------\n\nAfter you have fully migrated to using the new resources and the\nCloud Translation API, you can remove your legacy resources so that you only have a\nsingle set of resources to work with.\n\n1. Go to the Cloud Translation console.\n\n [Go to the\n Translation page](https://console.cloud.google.com/translation)\n2. In the navigation pane, click **Datasets** to view legacy datasets.\n\n3. For each dataset in the **Legacy datasets** table, select more_vert **More \\\u003e Delete** and then click\n **Confirm**.\n\n4. In the navigation pane, click **Models** to view legacy models.\n\n5. For each model in the **Legacy models** table, select more_vert **More \\\u003e Delete** and then click\n **Confirm**."]]
